APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

2018-08-29 · EMNLP 2018 10 · Yang Gao, Christian M. Meyer, Iryna Gurevych

We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users' preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/emnlp2018-april.

📄 PDF Abstract BibTeX arXiv:1808.09658

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UKPLab/emnlp2018-april 공식 구현

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Active Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Preference-based Interactive Multi-Document Summarisation

2019-06-07 · Yang Gao, Christian M. Meyer, Iryna Gurevych

Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound. Preference-based interactive learning has been successfully applied, but the existing methods require seve…

Active Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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2021-08-01 · ACL (InterNLP) 2021 8 · Yuxuan Ye, Edwin Simpson

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Articles reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documen…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning

2021-12-30 · Markus Peschl, Arkady Zgonnikov, Frans A. Oliehoek, Luciano C. Siebert

Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions. However, state-of-the art methods typically focus o…

Active Learning Ethics Reinforcement Learning (RL)

Interactively Learning Preference Constraints in Linear Bandits

2022-06-10 · David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause

We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, such as safe and comfortable driving behavi…

Decision Making Sequential Decision Making